Summer Internship
Navin Ramanan
6/01/2022
##Setup
Map Ensembl ID to Gene Symbol in the counts file
Write Counts Data with Gene Symbol to CSV
Reading and filtering Hippocampus count and metadata files
Reading and filtering Cortex count and metadata files
Cortex: APP (APP vs WT) only - No Covariates (~APP)
## Cortex: Running DESeq2 for APP (APP vs WT) only - No Covariates:padj < 0.05 and 0.10## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 446 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## resultsNames:## [1] "Intercept" "APP_APP_vs_WT"
##
## out of 34797 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4493, 13%
## LFC < 0 (down) : 4383, 13%
## outliers [1] : 0, 0%
## low counts [2] : 8852, 25%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results## log2 fold change (MLE): APP APP vs WT
## Wald test p-value: APP APP vs WT
## DataFrame with 10 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000068129 1858.8971 7.63594 0.252402 30.2530 4.76057e-201
## ENSMUSG00000079293 823.4879 6.23519 0.225957 27.5946 1.29026e-167
## ENSMUSG00000030789 1139.3238 6.46825 0.246666 26.2227 1.46409e-151
## ENSMUSG00000018927 788.3634 3.97689 0.152227 26.1247 1.90955e-150
## ENSMUSG00000000982 183.1320 4.56670 0.220314 20.7281 1.93133e-95
## ENSMUSG00000018930 37.1405 4.06970 0.202652 20.0822 1.05540e-89
## ENSMUSG00000046805 6682.6045 2.10279 0.105483 19.9349 2.02842e-88
## ENSMUSG00000018774 1709.7846 2.44287 0.124775 19.5782 2.37418e-85
## ENSMUSG00000097415 416.7944 2.21973 0.114782 19.3387 2.53764e-83
## ENSMUSG00000000682 382.4139 2.73088 0.141995 19.2322 1.98926e-82
## padj
## <numeric>
## ENSMUSG00000068129 1.23908e-196
## ENSMUSG00000079293 1.67914e-163
## ENSMUSG00000030789 1.27024e-147
## ENSMUSG00000018927 1.24255e-146
## ENSMUSG00000000982 1.00538e-91
## ENSMUSG00000018930 4.57831e-86
## ENSMUSG00000046805 7.54225e-85
## ENSMUSG00000018774 7.72439e-82
## ENSMUSG00000097415 7.33885e-80
## ENSMUSG00000000682 5.17765e-79## log2 fold change (MLE): APP APP vs WT
## Wald test p-value: APP APP vs WT
## DataFrame with 34880 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000068129 1858.897 7.63594 0.252402 30.2530 4.76057e-201
## ENSMUSG00000079293 823.488 6.23519 0.225957 27.5946 1.29026e-167
## ENSMUSG00000030789 1139.324 6.46825 0.246666 26.2227 1.46409e-151
## ENSMUSG00000018927 788.363 3.97689 0.152227 26.1247 1.90955e-150
## ENSMUSG00000000982 183.132 4.56670 0.220314 20.7281 1.93133e-95
## ... ... ... ... ... ...
## ENSMUSG00000064359 0.161461 -0.1461917 1.42846 -0.1023419 0.918485
## ENSMUSG00000064366 0.117536 -0.0179202 1.39008 -0.0128914 0.989714
## ENSMUSG00000095672 0.103382 0.3503108 1.63587 0.2141436 0.830435
## ENSMUSG00000079222 0.124061 0.3438527 1.71212 0.2008341 0.840828
## ENSMUSG00000079794 0.341101 -0.7071001 1.00281 -0.7051189 0.480736
## padj
## <numeric>
## ENSMUSG00000068129 1.23908e-196
## ENSMUSG00000079293 1.67914e-163
## ENSMUSG00000030789 1.27024e-147
## ENSMUSG00000018927 1.24255e-146
## ENSMUSG00000000982 1.00538e-91
## ... ...
## ENSMUSG00000064359 NA
## ENSMUSG00000064366 NA
## ENSMUSG00000095672 NA
## ENSMUSG00000079222 NA
## ENSMUSG00000079794 NA## class: DESeqDataSet
## dim: 34880 96
## metadata(1): version
## assays(6): counts mu ... replaceCounts replaceCooks
## rownames(34880): ENSMUSG00000102628 ENSMUSG00000097426 ...
## ENSMUSG00000095041 ENSMUSG00000095742
## rowData names(23): baseMean baseVar ... maxCooks replace
## colnames(96): 102 103 ... 95 96
## colData names(6): Sex Diet ... sizeFactor replaceable## Cortex DESeq2 result is saved in file: 'Cortex_deseq_results_APP_only.csv'## Cortex DESeq2 normalized counts is saved in file: 'Cortex_deseq_norm_counts_APP_only.csv'## Cortex DESeq2 result with Gene names (mgi_symbols) is saved in file: 'Cortex_deseq_results_with_genename_APP_only.csv'## [1] 34880## [1] 7419## Cortex DESeq2 result after padj (0.05) filtering is saved in file: 'Cortex_deseq_results_APP_only_padj_05_filtered.csv'## Number of DE genes significant at padj < 0.05 for Cortex:APP (APP vs WT): 7419## Number of DE genes discarded after padj threshold 0.05 filtering for Cortex:APP(APP vs WT): 27461
## [1] 34880## [1] 8876## Cortex DESeq2 result after padj (0.1) filtering is saved in file: 'Cortex_deseq_results_APP_only_padj_1_filtered.csv'## Number of DE genes significant at padj < 0.1 for Cortex:APP (APP vs WT): 8876## Number of DE genes discarded after padj threshold 0.1 filtering for Cortex:APP(APP vs WT) : 26004
- Cortex: Enhanced Volcano Plot
## Warning: ggrepel: 7394 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
- Cortex: Heatmap
8 Cortex: PCA
## ************** Principal Component Analysis (PCA) **************## class: DESeqTransform
## dim: 34880 96
## metadata(1): version
## assays(1): ''
## rownames(34880): ENSMUSG00000102628 ENSMUSG00000097426 ...
## ENSMUSG00000095041 ENSMUSG00000095742
## rowData names(23): baseMean baseVar ... maxCooks replace
## colnames(96): 102 103 ... 95 96
## colData names(6): Sex Diet ... sizeFactor replaceable## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 62.0691 47.2091 43.43561 39.2192 30.52674 28.06197
## Proportion of Variance 0.1105 0.0639 0.05409 0.0441 0.02672 0.02258
## Cumulative Proportion 0.1105 0.1744 0.22844 0.2725 0.29925 0.32183
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 27.87897 26.57108 25.07750 24.19879 23.78723 22.48140
## Proportion of Variance 0.02228 0.02024 0.01803 0.01679 0.01622 0.01449
## Cumulative Proportion 0.34411 0.36435 0.38238 0.39917 0.41540 0.42989
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 21.64538 20.17774 19.77058 19.23614 19.02146 18.7665
## Proportion of Variance 0.01343 0.01167 0.01121 0.01061 0.01037 0.0101
## Cumulative Proportion 0.44332 0.45499 0.46620 0.47681 0.48718 0.4973
## PC19 PC20 PC21 PC22 PC23 PC24
## Standard deviation 18.42451 18.17533 18.04444 17.78258 17.60624 17.38005
## Proportion of Variance 0.00973 0.00947 0.00933 0.00907 0.00889 0.00866
## Cumulative Proportion 0.50701 0.51648 0.52581 0.53488 0.54377 0.55243
## PC25 PC26 PC27 PC28 PC29 PC30
## Standard deviation 17.26232 17.19011 16.97787 16.92400 16.83790 16.72540
## Proportion of Variance 0.00854 0.00847 0.00826 0.00821 0.00813 0.00802
## Cumulative Proportion 0.56097 0.56944 0.57771 0.58592 0.59405 0.60207
## PC31 PC32 PC33 PC34 PC35 PC36
## Standard deviation 16.56356 16.40628 16.36023 16.31078 16.11885 16.01803
## Proportion of Variance 0.00787 0.00772 0.00767 0.00763 0.00745 0.00736
## Cumulative Proportion 0.60993 0.61765 0.62532 0.63295 0.64040 0.64775
## PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 15.94019 15.85598 15.83916 15.78594 15.77478 15.65578
## Proportion of Variance 0.00728 0.00721 0.00719 0.00714 0.00713 0.00703
## Cumulative Proportion 0.65504 0.66225 0.66944 0.67658 0.68372 0.69074
## PC43 PC44 PC45 PC46 PC47 PC48
## Standard deviation 15.61466 15.55155 15.47870 15.43806 15.36342 15.32200
## Proportion of Variance 0.00699 0.00693 0.00687 0.00683 0.00677 0.00673
## Cumulative Proportion 0.69774 0.70467 0.71154 0.71837 0.72514 0.73187
## PC49 PC50 PC51 PC52 PC53 PC54
## Standard deviation 15.27167 15.24753 15.19989 15.12776 15.10515 15.04049
## Proportion of Variance 0.00669 0.00667 0.00662 0.00656 0.00654 0.00649
## Cumulative Proportion 0.73855 0.74522 0.75184 0.75841 0.76495 0.77143
## PC55 PC56 PC57 PC58 PC59 PC60
## Standard deviation 15.00100 14.98112 14.89375 14.83836 14.8294 14.81184
## Proportion of Variance 0.00645 0.00643 0.00636 0.00631 0.0063 0.00629
## Cumulative Proportion 0.77788 0.78432 0.79068 0.79699 0.8033 0.80958
## PC61 PC62 PC63 PC64 PC65 PC66
## Standard deviation 14.72891 14.7073 14.69490 14.60399 14.5829 14.52986
## Proportion of Variance 0.00622 0.0062 0.00619 0.00611 0.0061 0.00605
## Cumulative Proportion 0.81580 0.8220 0.82820 0.83431 0.8404 0.84646
## PC67 PC68 PC69 PC70 PC71 PC72
## Standard deviation 14.50349 14.42430 14.39881 14.32419 14.29788 14.27752
## Proportion of Variance 0.00603 0.00597 0.00594 0.00588 0.00586 0.00584
## Cumulative Proportion 0.85249 0.85846 0.86440 0.87028 0.87614 0.88199
## PC73 PC74 PC75 PC76 PC77 PC78
## Standard deviation 14.2207 14.14266 14.11283 14.06282 14.04241 13.9728
## Proportion of Variance 0.0058 0.00573 0.00571 0.00567 0.00565 0.0056
## Cumulative Proportion 0.8878 0.89352 0.89923 0.90490 0.91055 0.9162
## PC79 PC80 PC81 PC82 PC83 PC84
## Standard deviation 13.90497 13.85897 13.76516 13.73979 13.62045 13.5923
## Proportion of Variance 0.00554 0.00551 0.00543 0.00541 0.00532 0.0053
## Cumulative Proportion 0.92169 0.92720 0.93263 0.93805 0.94336 0.9487
## PC85 PC86 PC87 PC88 PC89 PC90
## Standard deviation 13.52340 13.48809 13.37789 13.17780 12.99373 12.84734
## Proportion of Variance 0.00524 0.00522 0.00513 0.00498 0.00484 0.00473
## Cumulative Proportion 0.95390 0.95912 0.96425 0.96923 0.97407 0.97880
## PC91 PC92 PC93 PC94 PC95 PC96
## Standard deviation 12.72544 12.51280 12.35435 11.73444 11.42502 3.713e-13
## Proportion of Variance 0.00464 0.00449 0.00438 0.00395 0.00374 0.000e+00
## Cumulative Proportion 0.98345 0.98793 0.99231 0.99626 1.00000 1.000e+00## [1] 1.104523e-01 6.389620e-02 5.408981e-02 4.409825e-02 2.671680e-02
## [6] 2.257666e-02 2.228316e-02 2.024147e-02 1.802984e-02 1.678846e-02
## [11] 1.622225e-02 1.449006e-02 1.343241e-02 1.167263e-02 1.120630e-02
## [16] 1.060863e-02 1.037316e-02 1.009699e-02 9.732303e-03 9.470829e-03
## [21] 9.334919e-03 9.065941e-03 8.887031e-03 8.660152e-03 8.543222e-03
## [26] 8.471899e-03 8.263990e-03 8.211633e-03 8.128294e-03 8.020042e-03
## [31] 7.865585e-03 7.716912e-03 7.673654e-03 7.627341e-03 7.448890e-03
## [36] 7.355997e-03 7.284677e-03 7.207920e-03 7.192632e-03 7.144382e-03
## [41] 7.134284e-03 7.027045e-03 6.990184e-03 6.933794e-03 6.868981e-03
## [46] 6.832965e-03 6.767046e-03 6.730607e-03 6.686469e-03 6.665340e-03
## [51] 6.623758e-03 6.561040e-03 6.541448e-03 6.485561e-03 6.451549e-03
## [56] 6.434456e-03 6.359627e-03 6.312414e-03 6.304828e-03 6.289864e-03
## [61] 6.219631e-03 6.201412e-03 6.190941e-03 6.114583e-03 6.096942e-03
## [66] 6.052662e-03 6.030712e-03 5.965036e-03 5.943972e-03 5.882525e-03
## [71] 5.860931e-03 5.844257e-03 5.797795e-03 5.734371e-03 5.710205e-03
## [76] 5.669810e-03 5.653365e-03 5.597449e-03 5.543242e-03 5.506624e-03
## [81] 5.432328e-03 5.412325e-03 5.318715e-03 5.296717e-03 5.243187e-03
## [86] 5.215842e-03 5.130965e-03 4.978622e-03 4.840509e-03 4.732060e-03
## [91] 4.642685e-03 4.488825e-03 4.375861e-03 3.947741e-03 3.742292e-03
## [96] 3.951672e-30## # A tibble: 5 × 3
## variance_explained principal_components cumulative
## <dbl> <chr> <dbl>
## 1 0.110 PC1 0.110
## 2 0.0639 PC2 0.174
## 3 0.0541 PC3 0.228
## 4 0.0441 PC4 0.273
## 5 0.0267 PC5 0.299
- Hippocampus: APP (APP vs WT) only - No Covariates (~APP)
## Hippocampus: Running DESeq2 for APP (APP vs WT):padj < 0.05 and 0.10## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 321 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing## resultsNames:## [1] "Intercept" "APP_APP_vs_WT"
##
## out of 33266 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4297, 13%
## LFC < 0 (down) : 4292, 13%
## outliers [1] : 0, 0%
## low counts [2] : 12259, 37%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results## log2 fold change (MLE): APP APP vs WT
## Wald test p-value: APP APP vs WT
## DataFrame with 10 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000068129 933.4821 7.16600 0.278978 25.6866 1.64998e-145
## ENSMUSG00000079293 468.4345 4.70708 0.217993 21.5928 2.09802e-103
## ENSMUSG00000030789 453.6241 5.58720 0.272981 20.4673 4.21204e-93
## ENSMUSG00000018927 440.8573 3.04395 0.162382 18.7456 2.10212e-78
## ENSMUSG00000000982 80.2961 3.76934 0.235526 16.0039 1.19998e-57
## ENSMUSG00000046805 4465.5369 1.69614 0.110259 15.3832 2.12193e-53
## ENSMUSG00000000682 187.4863 2.62909 0.174659 15.0527 3.31469e-51
## ENSMUSG00000021423 1263.4021 1.60099 0.110871 14.4402 2.89104e-47
## ENSMUSG00000040552 308.6785 1.70861 0.120614 14.1659 1.48931e-45
## ENSMUSG00000097415 303.0665 1.69829 0.120206 14.1281 2.54864e-45
## padj
## <numeric>
## ENSMUSG00000068129 3.46695e-141
## ENSMUSG00000079293 2.20418e-99
## ENSMUSG00000030789 2.95011e-89
## ENSMUSG00000018927 1.10424e-74
## ENSMUSG00000000982 5.04278e-54
## ENSMUSG00000046805 7.43101e-50
## ENSMUSG00000000682 9.94977e-48
## ENSMUSG00000021423 7.59331e-44
## ENSMUSG00000040552 3.47704e-42
## ENSMUSG00000097415 5.35521e-42## log2 fold change (MLE): APP APP vs WT
## Wald test p-value: APP APP vs WT
## DataFrame with 33271 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000068129 933.4821 7.16600 0.278978 25.6866 1.64998e-145
## ENSMUSG00000079293 468.4345 4.70708 0.217993 21.5928 2.09802e-103
## ENSMUSG00000030789 453.6241 5.58720 0.272981 20.4673 4.21204e-93
## ENSMUSG00000018927 440.8573 3.04395 0.162382 18.7456 2.10212e-78
## ENSMUSG00000000982 80.2961 3.76934 0.235526 16.0039 1.19998e-57
## ... ... ... ... ... ...
## ENSMUSG00000064369 2.5058343 -0.3644526 0.257539 -1.4151343 0.1570291
## ENSMUSG00000079190 0.2880846 0.9808730 1.373280 0.7142555 0.4750692
## ENSMUSG00000079222 0.0739574 0.0800712 2.844467 0.0281498 0.9775427
## ENSMUSG00000062783 0.1831708 0.4845350 1.573588 0.3079172 0.7581453
## ENSMUSG00000079808 0.7429405 1.3749869 0.721256 1.9063780 0.0566012
## padj
## <numeric>
## ENSMUSG00000068129 3.46695e-141
## ENSMUSG00000079293 2.20418e-99
## ENSMUSG00000030789 2.95011e-89
## ENSMUSG00000018927 1.10424e-74
## ENSMUSG00000000982 5.04278e-54
## ... ...
## ENSMUSG00000064369 NA
## ENSMUSG00000079190 NA
## ENSMUSG00000079222 NA
## ENSMUSG00000062783 NA
## ENSMUSG00000079808 NA## class: DESeqDataSet
## dim: 33271 96
## metadata(1): version
## assays(6): counts mu ... replaceCounts replaceCooks
## rownames(33271): ENSMUSG00000102628 ENSMUSG00000086053 ...
## ENSMUSG00000095041 ENSMUSG00000095742
## rowData names(23): baseMean baseVar ... maxCooks replace
## colnames(96): 102 103 ... 95 96
## colData names(6): Sex Diet ... sizeFactor replaceable## Hippocampus DESeq2 result is saved in file: 'Hippocampus_deseq_results_APP_only.csv'## Hippocampus DESeq2 normalized counts is saved in file: 'Hippocampus_deseq_norm_counts_APP_only.csv'## Hippocampus DESeq2 result with Gene names (mgi_symbols) is saved in file: 'Hippocampus_deseq_results_with_genename_APP_only.csv'## Hippocampus DESeq2 result after padj (0.05) filtering is saved in file: 'Hippocampus_deseq_results_APP_only_padj_05_filtered.csv'## Number of DE genes significant at padj < 0.05 for Hippocampus:APP (APP vs WT): 6292## Number of DE genes discarded after padj threshold < 0.05 filtering for Hippocampus:APP (APP vs WT): 26979
## Hippocampus DESeq2 result after padj (0.1) filtering is saved in file: 'Hippocampus_deseq_results_APP_only_padj_1_filtered.csv'## Number of DE genes significant at padj < 0.1 for Hippocampus:APP (APP vs WT): 8589## Number of DE genes discarded after padj threshold < 0.1 filtering for Hippocampus:APP(APP vs WT) only: 24682
- Hippocampus: Enhanced Volcano Plot
## Warning: ggrepel: 6274 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
- Hippocampus: Heatmap
12 Hippocampus: PCA
## ************** Principal Component Analysis (PCA) **************## class: DESeqTransform
## dim: 33271 96
## metadata(1): version
## assays(1): ''
## rownames(33271): ENSMUSG00000102628 ENSMUSG00000086053 ...
## ENSMUSG00000095041 ENSMUSG00000095742
## rowData names(23): baseMean baseVar ... maxCooks replace
## colnames(96): 102 103 ... 95 96
## colData names(6): Sex Diet ... sizeFactor replaceable## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 90.3374 40.46986 32.62333 29.76544 26.69543 25.7985
## Proportion of Variance 0.2453 0.04923 0.03199 0.02663 0.02142 0.0200
## Cumulative Proportion 0.2453 0.29451 0.32650 0.35313 0.37455 0.3946
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 22.4106 21.61071 20.40882 19.90844 19.61700 19.1281
## Proportion of Variance 0.0151 0.01404 0.01252 0.01191 0.01157 0.0110
## Cumulative Proportion 0.4097 0.42368 0.43620 0.44812 0.45968 0.4707
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 18.65039 18.45338 18.06351 17.53585 17.26056 16.77386
## Proportion of Variance 0.01045 0.01023 0.00981 0.00924 0.00895 0.00846
## Cumulative Proportion 0.48113 0.49137 0.50118 0.51042 0.51937 0.52783
## PC19 PC20 PC21 PC22 PC23 PC24
## Standard deviation 16.66584 16.55476 16.2123 16.16840 16.07476 15.98455
## Proportion of Variance 0.00835 0.00824 0.0079 0.00786 0.00777 0.00768
## Cumulative Proportion 0.53618 0.54441 0.5523 0.56017 0.56794 0.57562
## PC25 PC26 PC27 PC28 PC29 PC30
## Standard deviation 15.7913 15.76898 15.74085 15.65944 15.59203 15.47032
## Proportion of Variance 0.0075 0.00747 0.00745 0.00737 0.00731 0.00719
## Cumulative Proportion 0.5831 0.59059 0.59803 0.60540 0.61271 0.61990
## PC31 PC32 PC33 PC34 PC35 PC36
## Standard deviation 15.45627 15.35796 15.26783 15.22922 15.22245 15.1526
## Proportion of Variance 0.00718 0.00709 0.00701 0.00697 0.00696 0.0069
## Cumulative Proportion 0.62708 0.63417 0.64118 0.64815 0.65512 0.6620
## PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 15.11612 15.08163 15.01432 14.94051 14.90765 14.85063
## Proportion of Variance 0.00687 0.00684 0.00678 0.00671 0.00668 0.00663
## Cumulative Proportion 0.66888 0.67572 0.68250 0.68921 0.69589 0.70251
## PC43 PC44 PC45 PC46 PC47 PC48
## Standard deviation 14.83491 14.77117 14.71755 14.7031 14.5899 14.57080
## Proportion of Variance 0.00661 0.00656 0.00651 0.0065 0.0064 0.00638
## Cumulative Proportion 0.70913 0.71569 0.72220 0.7287 0.7351 0.74147
## PC49 PC50 PC51 PC52 PC53 PC54
## Standard deviation 14.52834 14.48721 14.44832 14.43433 14.40562 14.33001
## Proportion of Variance 0.00634 0.00631 0.00627 0.00626 0.00624 0.00617
## Cumulative Proportion 0.74782 0.75413 0.76040 0.76666 0.77290 0.77907
## PC55 PC56 PC57 PC58 PC59 PC60
## Standard deviation 14.29824 14.23271 14.20749 14.19897 14.14211 14.11407
## Proportion of Variance 0.00614 0.00609 0.00607 0.00606 0.00601 0.00599
## Cumulative Proportion 0.78522 0.79130 0.79737 0.80343 0.80944 0.81543
## PC61 PC62 PC63 PC64 PC65 PC66
## Standard deviation 14.09262 14.05855 13.98753 13.96748 13.93105 13.90883
## Proportion of Variance 0.00597 0.00594 0.00588 0.00586 0.00583 0.00581
## Cumulative Proportion 0.82140 0.82734 0.83322 0.83908 0.84492 0.85073
## PC67 PC68 PC69 PC70 PC71 PC72
## Standard deviation 13.80496 13.75903 13.73146 13.68519 13.64327 13.60492
## Proportion of Variance 0.00573 0.00569 0.00567 0.00563 0.00559 0.00556
## Cumulative Proportion 0.85646 0.86215 0.86782 0.87345 0.87904 0.88460
## PC73 PC74 PC75 PC76 PC77 PC78
## Standard deviation 13.5271 13.45292 13.41007 13.39542 13.34023 13.31905
## Proportion of Variance 0.0055 0.00544 0.00541 0.00539 0.00535 0.00533
## Cumulative Proportion 0.8901 0.89554 0.90095 0.90634 0.91169 0.91702
## PC79 PC80 PC81 PC82 PC83 PC84
## Standard deviation 13.25343 13.20297 13.16718 13.11305 13.05575 12.99794
## Proportion of Variance 0.00528 0.00524 0.00521 0.00517 0.00512 0.00508
## Cumulative Proportion 0.92230 0.92754 0.93275 0.93792 0.94304 0.94812
## PC85 PC86 PC87 PC88 PC89 PC90
## Standard deviation 12.91775 12.87196 12.78578 12.7670 12.70706 12.60887
## Proportion of Variance 0.00502 0.00498 0.00491 0.0049 0.00485 0.00478
## Cumulative Proportion 0.95314 0.95812 0.96303 0.9679 0.97278 0.97756
## PC91 PC92 PC93 PC94 PC95 PC96
## Standard deviation 12.48854 12.46730 12.44995 11.90737 11.76471 2.458e-13
## Proportion of Variance 0.00469 0.00467 0.00466 0.00426 0.00416 0.000e+00
## Cumulative Proportion 0.98225 0.98692 0.99158 0.99584 1.00000 1.000e+00## [1] 2.452838e-01 4.922635e-02 3.198827e-02 2.662923e-02 2.141943e-02
## [6] 2.000425e-02 1.509528e-02 1.403693e-02 1.251901e-02 1.191265e-02
## [11] 1.156643e-02 1.099715e-02 1.045467e-02 1.023495e-02 9.807052e-03
## [16] 9.242464e-03 8.954553e-03 8.456684e-03 8.348116e-03 8.237204e-03
## [21] 7.899945e-03 7.857205e-03 7.766457e-03 7.679536e-03 7.495002e-03
## [26] 7.473797e-03 7.447158e-03 7.370326e-03 7.307010e-03 7.193371e-03
## [31] 7.180312e-03 7.089265e-03 7.006298e-03 6.970914e-03 6.964709e-03
## [36] 6.900909e-03 6.867754e-03 6.836449e-03 6.775561e-03 6.709111e-03
## [41] 6.679634e-03 6.628630e-03 6.614608e-03 6.557889e-03 6.510362e-03
## [46] 6.497571e-03 6.397930e-03 6.381177e-03 6.344046e-03 6.308175e-03
## [51] 6.274350e-03 6.262211e-03 6.237323e-03 6.172016e-03 6.144683e-03
## [56] 6.088482e-03 6.066932e-03 6.059651e-03 6.011217e-03 5.987405e-03
## [61] 5.969217e-03 5.940395e-03 5.880525e-03 5.863676e-03 5.833135e-03
## [66] 5.814541e-03 5.728022e-03 5.689970e-03 5.667192e-03 5.629055e-03
## [71] 5.594622e-03 5.563216e-03 5.499717e-03 5.439603e-03 5.405009e-03
## [76] 5.393200e-03 5.348855e-03 5.331880e-03 5.279473e-03 5.239347e-03
## [81] 5.210986e-03 5.168228e-03 5.123162e-03 5.077892e-03 5.015426e-03
## [86] 4.979936e-03 4.913476e-03 4.899035e-03 4.853160e-03 4.778443e-03
## [91] 4.687677e-03 4.671740e-03 4.658749e-03 4.261530e-03 4.160034e-03
## [96] 1.815907e-30## # A tibble: 5 × 3
## variance_explained principal_components cumulative
## <dbl> <chr> <dbl>
## 1 0.245 PC1 0.245
## 2 0.0492 PC2 0.295
## 3 0.0320 PC3 0.326
## 4 0.0266 PC4 0.353
## 5 0.0214 PC5 0.375
- GSEA:Running fgsea
Perform a GSEA using a ranked list log2FoldChange values for all genes discovered in the DESeq2 results against the M2 Canonical Pathways gene set collection. Convert gene identifiers in the results to the appropriate matching format found in the M2 gene sets. fgsea expects a named vector of gene level statistics and the gene sets in the form of a named list.
Using “m2.cp.wikipathways.v0.3.symbols.gmt” which is the Canonical Pathways gene sets derived from the WikiPathways pathway database (WikiPathways subset of CP)
## # A tibble: 34,880 × 8
## genes volc_plo…¹ log2F…² padj baseM…³ lfcSE stat pvalue
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ENSMUSG00000068129 UP 7.64 1.24e-196 1859. 0.252 30.3 4.76e-201
## 2 ENSMUSG00000079293 UP 6.24 1.68e-163 823. 0.226 27.6 1.29e-167
## 3 ENSMUSG00000030789 UP 6.47 1.27e-147 1139. 0.247 26.2 1.46e-151
## 4 ENSMUSG00000018927 UP 3.98 1.24e-146 788. 0.152 26.1 1.91e-150
## 5 ENSMUSG00000000982 UP 4.57 1.01e- 91 183. 0.220 20.7 1.93e- 95
## 6 ENSMUSG00000018930 UP 4.07 4.58e- 86 37.1 0.203 20.1 1.06e- 89
## 7 ENSMUSG00000046805 UP 2.10 7.54e- 85 6683. 0.105 19.9 2.03e- 88
## 8 ENSMUSG00000018774 UP 2.44 7.72e- 82 1710. 0.125 19.6 2.37e- 85
## 9 ENSMUSG00000097415 UP 2.22 7.34e- 80 417. 0.115 19.3 2.54e- 83
## 10 ENSMUSG00000000682 UP 2.73 5.18e- 79 382. 0.142 19.2 1.99e- 82
## # … with 34,870 more rows, and abbreviated variable names ¹volc_plot_status,
## # ²log2FoldChange, ³baseMean
## # ℹ Use `print(n = ...)` to see more rows## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.14% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize,
## gseaParam, : There are duplicate gene names, fgsea may produce unexpected
## results.## # A tibble: 190 × 8
## pathway pval padj log2err ES NES size leadi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 WP_MONOAMINE_GPCRS 3.25e-5 6.17e-4 0.557 -0.647 -2.13 33 <chr>
## 2 WP_HYPOTHETICAL_NETWORK_F… 3.83e-5 6.62e-4 0.557 -0.645 -2.07 31 <chr>
## 3 WP_HYPOXIADEPENDENT_DIFFE… 9.20e-4 1.09e-2 0.477 -0.738 -1.96 13 <chr>
## 4 WP_HYPOXIADEPENDENT_SELFR… 1.51e-3 1.69e-2 0.455 -0.724 -1.92 13 <chr>
## 5 WP_SPLICING_FACTOR_NOVA_R… 4.64e-3 3.83e-2 0.407 -0.487 -1.69 41 <chr>
## 6 WP_GPCRS_CLASS_C_METABOTR… 2.63e-2 1.28e-1 0.352 -0.579 -1.60 15 <chr>
## 7 WP_HYPOXIADEPENDENT_PROLI… 3.85e-2 1.74e-1 0.322 -0.524 -1.53 19 <chr>
## 8 WP_ACETYLCHOLINE_SYNTHESIS 8.99e-2 3.42e-1 0.253 -0.688 -1.47 7 <chr>
## 9 WP_REGULATION_OF_CARDIAC_… 1.24e-1 4.37e-1 0.211 -0.687 -1.40 6 <chr>
## 10 WP_BIOGENIC_AMINE_SYNTHES… 2.30e-1 5.88e-1 0.178 -0.438 -1.21 15 <chr>
## # … with 180 more rows, and abbreviated variable name ¹leadingEdge
## # ℹ Use `print(n = ...)` to see more rows## Cortex NES ordered fgsea results saved in file: 'Cortex_NES_ordered_fgsea_results_APP_only_FC0.csv'
## # A tibble: 33,271 × 8
## genes volc_plo…¹ log2F…² padj baseM…³ lfcSE stat pvalue
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ENSMUSG00000068129 UP 7.17 3.47e-141 933. 0.279 25.7 1.65e-145
## 2 ENSMUSG00000079293 UP 4.71 2.20e- 99 468. 0.218 21.6 2.10e-103
## 3 ENSMUSG00000030789 UP 5.59 2.95e- 89 454. 0.273 20.5 4.21e- 93
## 4 ENSMUSG00000018927 UP 3.04 1.10e- 74 441. 0.162 18.7 2.10e- 78
## 5 ENSMUSG00000000982 UP 3.77 5.04e- 54 80.3 0.236 16.0 1.20e- 57
## 6 ENSMUSG00000046805 UP 1.70 7.43e- 50 4466. 0.110 15.4 2.12e- 53
## 7 ENSMUSG00000000682 UP 2.63 9.95e- 48 187. 0.175 15.1 3.31e- 51
## 8 ENSMUSG00000021423 UP 1.60 7.59e- 44 1263. 0.111 14.4 2.89e- 47
## 9 ENSMUSG00000040552 UP 1.71 3.48e- 42 309. 0.121 14.2 1.49e- 45
## 10 ENSMUSG00000097415 UP 1.70 5.36e- 42 303. 0.120 14.1 2.55e- 45
## # … with 33,261 more rows, and abbreviated variable names ¹volc_plot_status,
## # ²log2FoldChange, ³baseMean
## # ℹ Use `print(n = ...)` to see more rows## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are duplicate gene names, fgsea may produce unexpected results.## # A tibble: 190 × 8
## pathway pval padj log2err ES NES size leadi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 WP_NONHOMOLOGOUS_END_JOINING 0.356 0.620 0.110 -0.531 -1.09 6 <chr>
## 2 WP_REGULATION_OF_CARDIAC_HYP… 0.5 0.714 0.0884 -0.484 -0.961 5 <chr>
## 3 WP_METHYLATION 0.681 0.880 0.0767 -0.348 -0.813 9 <chr>
## 4 WP_GLYCOGEN_METABOLISM 0.850 1 0.0779 -0.234 -0.770 34 <chr>
## 5 WP_LEPTIN_AND_ADIPONECTIN 0.856 1 0.0645 -0.275 -0.676 10 <chr>
## 6 WP_ONECARBON_METABOLISM_AND_… 0.967 1 0.0765 -0.182 -0.671 51 <chr>
## 7 WP_EXERCISEINDUCED_CIRCADIAN… 0.993 1 0.0755 -0.183 -0.663 49 <chr>
## 8 WP_SPLICING_FACTOR_NOVA_REGU… 0.976 1 0.0724 -0.183 -0.638 41 <chr>
## 9 WP_AMINO_ACID_METABOLISM 0.995 1 0.0926 -0.153 -0.635 96 <chr>
## 10 WP_FATTY_ACID_OMEGAOXIDATION 0.918 1 0.0585 -0.299 -0.614 6 <chr>
## # … with 180 more rows, and abbreviated variable name ¹leadingEdge
## # ℹ Use `print(n = ...)` to see more rows## Hippocampus NES ordered fgsea results saved in file: 'Hippocampus_NES_ordered_fgsea_results_APP_only_FC0.csv'
- Total DEGs Bar Graph
- FGSEA on Thomas CSV Batch_of_192_Trimmed_Hippo_DESeq2_Control_v_Supp_12mo_APP_DGE_Analysis_sex_controlled_padj_sorted_7_23_2022.csv
## # A tibble: 17,899 × 8
## genes volc_plot_…¹ log2F…² padj baseM…³ lfcSE stat pvalue
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ENSMUSG00000039617 DOWN -21.9 1.44e-8 6.91 3.06 -7.15 8.74e-13
## 2 ENSMUSG00000064293 UP 0.819 1.54e-8 491. 0.116 7.04 1.87e-12
## 3 ENSMUSG00000057455 UP 0.372 2.95e-6 1937. 0.0599 6.21 5.38e-10
## 4 ENSMUSG00000052861 DOWN -1.84 6.44e-6 108. 0.305 -6.04 1.56e- 9
## 5 ENSMUSG00000034467 DOWN -1.36 7.68e-6 47.5 0.227 -5.97 2.33e- 9
## 6 ENSMUSG00000055430 UP 0.529 1.17e-5 4625. 0.0900 5.87 4.26e- 9
## 7 ENSMUSG00000039155 DOWN -3.17 1.26e-5 24.6 0.542 -5.84 5.33e- 9
## 8 ENSMUSG00000037627 DOWN -1.77 1.59e-5 46.1 0.307 -5.77 7.73e- 9
## 9 ENSMUSG00000028546 UP 0.515 9.97e-5 1264. 0.0954 5.40 6.52e- 8
## 10 ENSMUSG00000039543 DOWN -1.46 9.97e-5 36.3 0.271 -5.40 6.66e- 8
## # … with 17,889 more rows, and abbreviated variable names ¹volc_plot_status,
## # ²log2FoldChange, ³baseMean
## # ℹ Use `print(n = ...)` to see more rows## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize,
## gseaParam, : There are duplicate gene names, fgsea may produce unexpected
## results.## # A tibble: 190 × 8
## pathway pval padj log2err ES NES size leadi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 WP_TYPE_II_INTERFERON_SIGNAL… 0.0138 0.830 0.381 -0.697 -1.68 29 <chr>
## 2 WP_MACROPHAGE_MARKERS 0.0413 0.830 0.322 -0.798 -1.58 10 <chr>
## 3 WP_EICOSANOID_METABOLISM_VIA… 0.0421 0.830 0.277 -0.634 -1.53 28 <chr>
## 4 WP_LEPTININSULIN_SIGNALING_O… 0.0795 0.830 0.288 -0.704 -1.53 16 <chr>
## 5 WP_INFLAMMATORY_RESPONSE_PAT… 0.0425 0.830 0.277 -0.634 -1.52 27 <chr>
## 6 WP_RETINOL_METABOLISM 0.0385 0.830 0.288 -0.607 -1.50 34 <chr>
## 7 WP_DYSREGULATED_MIRNA_TARGET… 0.0514 0.830 0.249 -0.620 -1.48 26 <chr>
## 8 WP_NUCLEOTIDE_GPCRS 0.0694 0.830 0.219 -0.721 -1.45 11 <chr>
## 9 WP_GLUTATHIONE_METABOLISM 0.0710 0.830 0.211 -0.649 -1.44 19 <chr>
## 10 WP_MATRIX_METALLOPROTEINASES 0.0862 0.830 0.190 -0.613 -1.41 22 <chr>
## # … with 180 more rows, and abbreviated variable name ¹leadingEdge
## # ℹ Use `print(n = ...)` to see more rows## THippo NES ordered fgsea results saved in file: 'THippo_NES_ordered_fgsea_results_APP_only_FC0.csv'## ************** FGSEA on Thomas CSV: log2FoldChange threshold = 0 **************
## # A tibble: 17,899 × 8
## genes volc_plot_…¹ log2F…² padj baseM…³ lfcSE stat pvalue
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ENSMUSG00000039617 DOWN -21.9 1.44e-8 6.91 3.06 -7.15 8.74e-13
## 2 ENSMUSG00000064293 DOWN 0.819 1.54e-8 491. 0.116 7.04 1.87e-12
## 3 ENSMUSG00000057455 DOWN 0.372 2.95e-6 1937. 0.0599 6.21 5.38e-10
## 4 ENSMUSG00000052861 DOWN -1.84 6.44e-6 108. 0.305 -6.04 1.56e- 9
## 5 ENSMUSG00000034467 DOWN -1.36 7.68e-6 47.5 0.227 -5.97 2.33e- 9
## 6 ENSMUSG00000055430 DOWN 0.529 1.17e-5 4625. 0.0900 5.87 4.26e- 9
## 7 ENSMUSG00000039155 DOWN -3.17 1.26e-5 24.6 0.542 -5.84 5.33e- 9
## 8 ENSMUSG00000037627 DOWN -1.77 1.59e-5 46.1 0.307 -5.77 7.73e- 9
## 9 ENSMUSG00000028546 DOWN 0.515 9.97e-5 1264. 0.0954 5.40 6.52e- 8
## 10 ENSMUSG00000039543 DOWN -1.46 9.97e-5 36.3 0.271 -5.40 6.66e- 8
## # … with 17,889 more rows, and abbreviated variable names ¹volc_plot_status,
## # ²log2FoldChange, ³baseMean
## # ℹ Use `print(n = ...)` to see more rows## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize,
## gseaParam, : There are duplicate gene names, fgsea may produce unexpected
## results.## # A tibble: 190 × 8
## pathway pval padj log2err ES NES size leadi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 WP_TYPE_II_INTERFERON_SIGNA… 0.00973 0.850 0.381 -0.697 -1.67 29 <chr>
## 2 WP_MACROPHAGE_MARKERS 0.0252 0.850 0.352 -0.798 -1.53 10 <chr>
## 3 WP_EICOSANOID_METABOLISM_VI… 0.0478 0.850 0.257 -0.634 -1.51 28 <chr>
## 4 WP_LEPTININSULIN_SIGNALING_… 0.0489 0.850 0.262 -0.704 -1.51 16 <chr>
## 5 WP_RETINOL_METABOLISM 0.0702 0.850 0.288 -0.607 -1.50 34 <chr>
## 6 WP_INFLAMMATORY_RESPONSE_PA… 0.0518 0.850 0.249 -0.634 -1.49 27 <chr>
## 7 WP_DYSREGULATED_MIRNA_TARGE… 0.0633 0.850 0.225 -0.620 -1.44 26 <chr>
## 8 WP_GLUTATHIONE_METABOLISM 0.0836 0.850 0.198 -0.649 -1.43 19 <chr>
## 9 WP_NUCLEOTIDE_GPCRS 0.0903 0.850 0.198 -0.721 -1.40 11 <chr>
## 10 WP_TYROBP_CAUSAL_NETWORK_IN… 0.0613 0.850 0.217 -0.515 -1.38 57 <chr>
## # … with 180 more rows, and abbreviated variable name ¹leadingEdge
## # ℹ Use `print(n = ...)` to see more rows## THippo NES ordered fgsea results saved in file: 'THippo_NES_ordered_fgsea_results_APP_only_FC1.csv'## ************** FGSEA on Thomas CSV: log2FoldChange threshold = 1 **************